Abstract
Monitoring, predicting and understanding traffic conditions in a city is an important problem for city planning and environmental monitoring. GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to have a unique view of the underlying dynamics of a city’s road network. In this paper, we propose a method to construct a model of traffic density based on large scale taxi traces. This model can be used to predict future traffic conditions and estimate the effect of emissions on the city’s air quality. We argue that considering traffic density on its own is insufficient for a deep understanding of the underlying traffic dynamics, and hence propose a novel method for automatically determining the capacity of each road segment. We evaluate our methods on a large scale database of taxi GPS logs and demonstrate their outstanding performance.
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References
Šingliar, T., Hauskrecht, M.: Modeling Highway Traffic Volumes. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 732–739. Springer, Heidelberg (2007)
Su, H., Yu, S.: Hybrid GA Based Online Support Vector Machine Model for Short-Term Traffic Flow Forecasting. In: Xu, M., Zhan, Y.-W., Cao, J., Liu, Y. (eds.) APPT 2007. LNCS, vol. 4847, pp. 743–752. Springer, Heidelberg (2007)
Lippi, M., Bertini, M., Frasconi, P.: Collective Traffic Forecasting. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part II. LNCS, vol. 6322, pp. 259–273. Springer, Heidelberg (2010)
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-Matching for Low-Sampling-Rate GPS Trajectories. In: Proceedings of ACM SIGSPATIAL (2009)
Chang, H., Tai, Y., Hsu, J.Y.: Context-aware taxi demand hotspots prediction. International Journal of Business Intelligence and Data Mining 5(1), 3–18 (2010)
Liu, S., Liu, Y., Ni, L.M., Fan, J., Li, M.: Towards Mobility-based Clustering. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010 (2010)
Hu, J., Cao, W., Luo, J., Yu, X.: Dynamic Modeling of Urban Population Travel Behavior based on Data Fusion of Mobile Phone Positioning Data and FCD. In: 17th International Conference on Geoinformatics (2009)
Liu, L., Biderman, A., Ratti, C.: Urban Mobility Landscape: Real Time Monitoring of Urban Mobility Patterns. In: Proceedings of the 11th International Conference on Computers in Urban Planning and Urban Management, CUPUM 2009 (2009)
Qi, G., Li, X., Li, S., Pan, G., Wang, Z.: Measuring Social Functions of City Regions from Large-scale Taxi Behaviors. In: PerCom Workshop (2011)
Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban Computing with Taxicabs. In: Proceedings of the 13th ACM International Conference on Ubiquitous Computing, UBICOMP 2011 (2011)
Chang, H., Tai, Y., Chen, H.W., Hsu, J.Y.: iTaxi: Context-Aware Taxi Demand Hotspots Prediction Using Ontology and Data Mining Approaches. In: Proceedings of the 13th Conference on Artificial Intelligence and Applications (TAAI 2008) (2008)
Lee, J., Shin, I., Park, G.L.: Analysis of the passenger pick-up pattern for taxi location recommendation. In: Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management, vol. 1 (2008)
Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers’ behavior patterns from their digital traces. Computers, Environment and Urban Systems 34, 541–548 (2010)
Phithakkitnukoon, S., Veloso, M., Bento, C., Biderman, A., Ratti, C.: Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 86–95. Springer, Heidelberg (2010)
Li, B., Zhang, D., Sun, L., Chen, C., Li, S., Qi, G., Yang, Q.: Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In: PerCom Workshops (2011)
Gühnemann, A., Schäfer, R., Thiessenhusen, K.: Monitoring traffic and emissions by floating car data. Institute of transport studies Australia (2004)
Wen, H., Hu, Z., Guo, J., Zhu, L., Sun, J.: Operational Analysis on Beijing Road Network during the Olympic Games. Journal of Transportation Systems Engineering and Information Technology 8(6), 32–37 (2008)
Schäfer, R.P., Thiessenhusen, K.U., Wagner, P.: A Traffic Information System by Means of Real-Time Floating-Car Data. In: 9th World Congress on Intelligent Transport Systems (2002)
Blandin, S., Ghaoui, L.E., Bayen, A.: Kernel regression for travel time estimation via convex optimization. In: Proceedings of the 48th IEEE Conference on Decision and Control (2009)
Scholkopf, B., Smola, A.: Learning with kernels. MIT press (2002)
Yuan, J., Zheng, Y.: T-Drive: Driving Directions Based on Taxi Trajectories. In: ACM SIGSPATIAL GIS (2010)
Herring, R., Hofleitner, A., Abbeel, P., Bayen, A.: Estimating arterial traffic conditions using sparse probe data. In: Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems (2010)
Brand, M.: Coupled hidden markov models for modeling interacting processes. Technical report, The Media Lab, Massachusetts Institute of Technology (1997)
Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with Knowledge from the Physical World. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011)
Yin, H., Wolfson, O.: A Weight-based map matching method in moving objects databases. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management (2004)
Alt, H., Efrat, A., Rote, G., Wenk, C.: Matching planar maps. Journal of Algorithms 49, 262–283 (2003)
Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: Proceedings of the 31st International Conference on Very Large Data Bases (2005)
Rogers, S., Langley, P., Wilson, C.: Mining GPS data to augment road models. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 104–113 (1999)
Edelkamp, S., Schrödl, S.: Route planning and map inference with global positioning traces, pp. 128–151. Springer-Verlag New York, Inc., New York (2003)
Chen, Y., Krumm, J.: Probabilistic modeling of traffic lanes from GPS traces. In: Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2010)
Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xie, X.: Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams. In: Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2011)
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Castro, P.S., Zhang, D., Li, S. (2012). Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds) Pervasive Computing. Pervasive 2012. Lecture Notes in Computer Science, vol 7319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31205-2_4
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DOI: https://doi.org/10.1007/978-3-642-31205-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31204-5
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